# LICENSE HEADER MANAGED BY add-license-header
#
# Copyright 2018 Kornia Team
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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from typing import Any, Dict, Optional

import torch

from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.enhance import normalize_min_max


class RandomAutoContrast(IntensityAugmentationBase2D):
    r"""Apply a random auto-contrast of a torch.Tensor image.

    Args:
        p: probability of applying the transformation.
        clip_output: if true clip output
        same_on_batch: apply the same transformation across the batch.
        keepdim: whether to keep the output shape the same as input (True) or broadcast it
                 to the batch form (False).
    Shape:
        - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`
        - Output: :math:`(B, C, H, W)`

    .. note::
        This function internally uses :func:`kornia.enhance.normalize_min_max`

    """

    def __init__(
        self, clip_output: bool = True, same_on_batch: bool = False, p: float = 1.0, keepdim: bool = False
    ) -> None:
        super().__init__(p=p, same_on_batch=same_on_batch, keepdim=keepdim)

        self.clip_output = clip_output

    def apply_transform(
        self,
        input: torch.Tensor,
        params: Dict[str, torch.Tensor],
        flags: Dict[str, Any],
        transform: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        out = normalize_min_max(input)

        if self.clip_output:
            return out.clamp(0.0, 1.0)

        return out
